# Topsis_Jashan_102017034
_for: **TOPSIS PACKAGE**_
_submitted by: **Jashanveer Kaur Dhillon**_
_Roll no: **102017034**_
_Group: **CS2**_
Topsis_Jashan_102017034 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems.
## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis-Jashan-102017034.
```bash
pip install Topsis_Jashan_102017034
```
## Usage
Firstly, install the Topsis_Jashan_102017034 package. After installation of the package, write the following command :-
```
from Topsis_Jashan_102017034.Topsis import topsis
```
topsis() function has four parameters ; The first parameter is for the input csv file , the second parameter is for weights , next parameter is for impacts and the last parameter is for the output csv file.
Example :-
topsis(input.csv,"1,1,1,1","+,-,+,+",output.csv)
Make sure that number of weights and impact is equal to the number of columns in input.csv
You can also run topsis from command line as follows :-
$ python [package name] [path of csv as string] [list of weights as string] [list of sign as string] [path of output file csv]
## Example
#### sample.csv
A csv file showing data for different mobile handsets having varying features.
| Model | Storage space(in gb) | Camera(in MP)| Price(in $) | Looks(out of 5) |
| :----: |:--------------------:|:------------:|:------------:|:---------------:|
| M1 | 16 | 12 | 250 | 5 |
| M2 | 16 | 8 | 200 | 3 |
| M3 | 32 | 16 | 300 | 4 |
| M4 | 32 | 8 | 275 | 4 |
| M5 | 16 | 16 | 225 | 2 |
weights vector = [ 1 , 1 , 1 , 1 ]
impacts vector = [ "+" , "+" , "-" , "+" ]
### input:
```python
topsis(input.csv,weights,impacts,result.csv)
```
### output:
```
TOPSIS RESULTS
-----------------------------
P-Score Rank
1 0.534277 3
2 0.308368 5
3 0.691632 1
4 0.534737 2
5 0.401046 4
```
## Other notes
* Make sure the csv does not contain categorical values
## License
[MIT](https://choosealicense.com/licenses/mit/)
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"description": "# Topsis_Jashan_102017034\r\n\r\n_for: **TOPSIS PACKAGE**_\r\n_submitted by: **Jashanveer Kaur Dhillon**_\r\n_Roll no: **102017034**_\r\n_Group: **CS2**_\r\n\r\n\r\nTopsis_Jashan_102017034 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems.\r\n\r\n## Installation\r\n\r\nUse the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis-Jashan-102017034.\r\n\r\n```bash\r\npip install Topsis_Jashan_102017034\r\n```\r\n\r\n## Usage\r\n\r\nFirstly, install the Topsis_Jashan_102017034 package. After installation of the package, write the following command :-\r\n```\r\nfrom Topsis_Jashan_102017034.Topsis import topsis\r\n```\r\ntopsis() function has four parameters ; The first parameter is for the input csv file , the second parameter is for weights , next parameter is for impacts and the last parameter is for the output csv file.\r\n\r\nExample :-\r\ntopsis(input.csv,\"1,1,1,1\",\"+,-,+,+\",output.csv)\r\n\r\nMake sure that number of weights and impact is equal to the number of columns in input.csv\r\n\r\nYou can also run topsis from command line as follows :-\r\n$ python [package name] [path of csv as string] [list of weights as string] [list of sign as string] [path of output file csv]\r\n\r\n\r\n\r\n## Example\r\n\r\n#### sample.csv\r\n\r\nA csv file showing data for different mobile handsets having varying features.\r\n\r\n| Model | Storage space(in gb) | Camera(in MP)| Price(in $) | Looks(out of 5) |\r\n| :----: |:--------------------:|:------------:|:------------:|:---------------:|\r\n| M1 | 16 | 12 | 250 | 5 |\r\n| M2 | 16 | 8 | 200 | 3 |\r\n| M3 | 32 | 16 | 300 | 4 |\r\n| M4 | 32 | 8 | 275 | 4 |\r\n| M5 | 16 | 16 | 225 | 2 |\r\n\r\nweights vector = [ 1 , 1 , 1 , 1 ]\r\n\r\nimpacts vector = [ \"+\" , \"+\" , \"-\" , \"+\" ]\r\n\r\n### input:\r\n\r\n```python\r\ntopsis(input.csv,weights,impacts,result.csv)\r\n```\r\n\r\n### output:\r\n```\r\n TOPSIS RESULTS\r\n-----------------------------\r\n\r\n P-Score Rank\r\n1 0.534277 3\r\n2 0.308368 5\r\n3 0.691632 1\r\n4 0.534737 2\r\n5 0.401046 4\r\n\r\n``` \r\n\r\n## Other notes\r\n* Make sure the csv does not contain categorical values\r\n\r\n\r\n## License\r\n[MIT](https://choosealicense.com/licenses/mit/)\r\n\r\n",
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